Business information service tool

ABSTRACT

The disclosure generally describes computer-implemented methods, software, and systems, including a method for providing suggestions. Transaction information is received that is associated with user actions during use by a user of a business intelligence tool. Each user action is associated with an operation in a particular stage of processing on business data obtained from one or more databases. The transaction information for a particular user action includes a user identifier identifying the user performing the particular user action, stage information, an associated operation, and parameters. The transaction information is stored. Subsequent user actions are monitored, including determining a time at which stage conditions match stage information in the stored transaction information. In response to determining matching stage conditions, pertinent transactions are identified. Suggestions are created. Each suggestion is associated with groups of one or more transactions of the pertinent transactions. The suggestions are provided for presentation to the user.

BACKGROUND

The present disclosure relates to information presentation.

Users of data may have different roles and thus may require differentuses of the same data. For example, a person in the accountingdepartment may have one use of accounting data, while an executive ofthe company might have a different use. Each of these users may usetheir own set of queries and/or other means tailored to their needs fordata.

SUMMARY

The disclosure generally describes computer-implemented methods,software, and systems for providing suggestions. For example,transaction information is received that is associated with user actionsduring use by a user of a business intelligence tool. Each user actionis associated with an operation in a particular stage of processing onbusiness data obtained from one or more databases. The transactioninformation for a particular user action includes, for example, a useridentifier identifying the user performing the particular user action,stage information identifying the particular stage of the processing inwhich the particular user action occurs, the operation associated withthe particular user action, and one or more parameters used in theoperation. The transaction information is stored. Subsequent useractions by the user are monitored, including determining a time at whichstage conditions associated with a current session for the user matchthe user and the stage information in the stored transactioninformation. In response to determining that matching stage conditionsexist, pertinent transactions associated with the matching stageconditions are identified. One or more suggestions are created forpresentation to the user. Each suggestion of the one or more suggestionsis associated with groups of one or more transactions of the pertinenttransactions. The one or more suggestions are provided for presentationto the user.

The present disclosure relates to computer-implemented methods,software, and systems for providing suggestions. Onecomputer-implemented method includes: receiving transaction informationassociated with user actions during use by a user of a businessintelligence tool, each user action associated with an operation in aparticular stage of processing on business data obtained from one ormore databases, the transaction information for a particular user actionincluding: a user identifier identifying the user performing theparticular user action, stage information identifying the particularstage of the processing in which the particular user action occurs, theoperation associated with the particular user action, and one or moreparameters used in the operation; storing the transaction information;monitoring subsequent user actions by the user, including determining atime at which stage conditions associated with a current session for theuser match the user and the stage information in the stored transactioninformation; and in response to determining that matching stageconditions exist: identifying pertinent transactions associated with thematching stage conditions, creating one or more suggestions forpresentation to the user, each suggestion of the one or more suggestionsbeing associated with groups of one or more transactions of thepertinent transactions, and providing the one or more suggestions forpresentation to the user.

In some implementations, self-service business intelligence (BI) toolscan be used, e.g., that provide access to the data in different ways bydifferent users and/or types of users. For example, one motive behindthe use and the evolution of self-service BI tools can be to increasethe ease of use for an end user, who may be an executive or a commonuser. In a typical scenario, for example, each of these end users canperform the same actions on different data from the same domain.

Self-service BI tools, such as desktop intelligence tools, can allowusers to view their data in the form of charts and graphs and hence spottrends in the data. However, creating such visual aids can generallytake a long time to create when done manually each time. For example,the user may have to filter the data according to certain requirements,choose a visual representation type, and finally save, display and sharethe completed result. For businesses performing a number of routine butlengthy activities, having an automated solution can act as a timesaver.

Consider an example of a mid-level manager of a pharmaceutical company.The manager may have several pharmacy stores to oversee. Every month,the manager may receive raw operating data from pharmacy stores and willneed to manipulate the data before presenting the data to the manager'ssuperiors. For each store, for example, some data pertaining to dailyfunctioning (e.g., daily wages and order details) may be of no use toupper management and may be removed. Certain formulas may also have tobe applied to calculate profit/loss, aggregate sales, and/or otherresults, and new columns may be created for the same. Once the desireddata is chosen, the data may have to be presented in an easilyunderstandable visual format and shared. This sequence typically wouldhave to be repeated for every store under the manager's control. In someimplementations, the manager's actions on the data can be tracked fromone store, and the tracked information can allow the manager to performthe same actions on the other related data more easily, e.g., with onlya few clicks, reducing the manager's workload.

Solutions to achieve these results can include, for example,implementing a software solution in which users, based on their role(e.g., as a client, a company executive, or a system administrator) arepresented with recommendations based on their most relevant actions. Forexample, user transactions can be registered and stored in a database.Based on the stored information, suggestions can be provided to help theuser save time on lengthy but routine activities. For example, insteadof having to manually filter the data, an extension can enable users todirectly select the required operations and parameters based on thechoices they had used the previous time. Specifically, whilerepresenting and sharing the final data, the software can suggest theuser's previously favored choices. Knowing which features are mostrelevant and automating them, businesses can prioritize and speed uptheir workflows and deprioritize the little used features to keep themout of the way.

Some implementations include corresponding computer systems, apparatus,and computer programs recorded on one or more computer storage devices,each configured to perform the actions of the methods. A system of oneor more computers can be configured to perform particular operations oractions by virtue of having software, firmware, hardware, or acombination of software, firmware, or hardware installed on the systemthat in operation causes (or causes the system) to perform the actions.One or more computer programs can be configured to perform particularoperations or actions by virtue of including instructions that, whenexecuted by data processing apparatus, cause the apparatus to performthe actions.

The foregoing and other implementations can each optionally include oneor more of the following features, alone or in combination. Inparticular, one implementation can include all the following features:

In a first aspect, combinable with any of the previous aspects, themethod further comprises, after providing the one or more suggestions:receiving an identification of a particular suggestion from the providedone or more suggestions; performing, using the identified particularsuggestion, a new operation using transaction information for theparticular suggestion in a new operation on business data beingmanipulated by the user, including using the one or more parameters fromthe stored transaction information; and updating the stored transactioninformation for the particular suggestion to indicate an additional useof an associated user action.

In a second aspect, combinable with any of the previous aspects, stagesare sequential and are selected from the group comprising a datapreparation stage in which data is prepared, a visualization stage inwhich data is presented in a visualization to the user, a synthesisstage in which a business story is created from one or morevisualizations, and a sharing stage in which business informationassociated with previous stages is shared.

In a third aspect, combinable with any of the previous aspects, useractions in the data preparation stage include actions selected from agroup comprising: selecting a dataset, selecting a column in the dataset, selecting a custom range in the data set, filtering the data set,sorting the data set, converting the data set, renaming the data set,create a measure or dimension for the data set, creating a hierarchy forthe data set, duplicating data in the data set, and grouping data byrange in the dataset.

In a fourth aspect, combinable with any of the previous aspects, themethod comprises ranking the suggestions to produce ranked suggestions.

In a fifth aspect, combinable with any of the previous aspects, eachranked suggestion is ranked according to a transaction rank determinedfrom one or more of a favorite index, a frequency count, and areferences count.

In a sixth aspect, combinable with any of the previous aspects, the oneor more suggestions provided to the user include, for each rankedsuggestion, a suggestion identifier, a feature name, the favorite index,the frequency count, the references count, one or more parameters, oneor more column names associated with the one or more parameters, a stagecategory, and a transaction rank.

In a seventh aspect, combinable with any of the previous aspects, eachranked suggestion is further ranked using a measure of time since anoccurrence of an associated user action.

The subject matter described in this specification can be implemented inparticular implementations so as to realize one or more of the followingadvantages. First, determining and automating most relevant features canhelp a business prioritize and speed up their workflows. Second, littleused features can be deprioritized to keep them out of the way.

The details of one or more implementations of the subject matter of thisspecification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of an environment for providing suggestions.

FIG. 2 is a flow diagram for an example process for recordingtransaction information for a user action.

FIG. 3 is a flow diagram for an example process for making suggestionsand processing a selected suggestion.

FIG. 4 shows an example user interface for working with business datafrom which transaction information is obtained.

FIGS. 5A-5B collectively show an example user interface for providingsuggestions and determining a suggestion selected by the user.

FIG. 6 is a diagram of an example hierarchical structure for maintainingtransaction information.

FIG. 7 is a flowchart of an example method for providing suggestion.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

This disclosure generally describes computer-implemented methods,software, and systems for providing suggestions. For example, thesuggestions can be provided to a user who is using a businessintelligence tool. The suggestions can be generated, for example, basedon prior transactions of the user. When one of the suggestions is chosenand accepted by the user, for example, the transaction is repeated,e.g., without requiring the user to re-formulate the command needed toset up the transaction.

In some implementations, suggestions can include recommendations thatare based on most relevant activities of a user. For example, relevanttransactions can be determined from transactions that occur for a userand the frequency of use by the user. The transaction information can bestored in a database, e.g., in a transaction repository that is laterused to suggest transactions to the user. The user can accept thesuggestion made, e.g., with a single click in a user interface. Onceselected, appropriate actions associated with the suggestion are appliedon the data. Suggestions provided to the user can be associated with astage of processing, e.g., in addition to previous actions taken by theuser. For example, the options available in a particular stage can beavailable to all succeeding stages while the reverse will not be true.For example, operations in a data preparation stage can be available insubsequent stages, but operations from a visualization are not availablein the data preparation stage. User actions can be constantly recordedover time, and new transactions can be added to the database, e.g., forsubsequent identification of suggestions. In some implementations,application code that supports the process of storing transactioninformation and identifying suggestions can be implemented as pluginsand/or other components. As a result, the following use cases can besupported.

In a use case associated with medical data, data may be handled bypatients, doctors, nurses, pharmaceutical executives, and/or otherpeople. A pharmaceutical executive, for example, can use businessintelligence tools or software to filter and process the data about aparticular brand of medicine or the sales in a particular area. Asimilar operation may be used to analyze the same kind of data fromanother region. Instead of having to manually set parameters andfilters, a single click recommendation can accomplish the same muchfaster.

In a use case associated with automobile data, the data may be handledby administrators, dealers and customers. Administrators, for example,may be able to add to the data and rename columns as well as merge data.A dealer, for example, may use the software to look for factors such asstores with high profits or losses, sales by vehicle, sales by segment,regions with highest returns, or other information.

In a use case associated with stock data, information may be used byboth stock brokers and by the customers. A stock broker, for example,can edit and update stock prices, search for a particular range ofstocks and make recommendations for buying and selling for his/herclient. The client, on the other hand, may be able to see the pastprices for stocks in their portfolio, see a range of stocks given aparameter, or decide on the amount of the portfolio to be bought orsold.

In a use case associated with data in the academic domain, someinformation may be of use to the higher officials in colleges anduniversities. Raw student data, for example, can be filtered to sortstudents by department, locate course information and grades, andidentify top-performing students. Lecturers can use the data aboutstudent marks to calculate median marks and create histograms for acourse. The data can finally be shared in a graphic form for students toview their relative rankings and for university officials to assess theprogress of the student body at regular intervals.

In a use case associated with corporate data, a managing director of alarge company may use data about his/her employees to measure progressand adherence to company goals. The data can be further filteredaccording to department, project, manager, and/or overall productivity.The managing director can view the relative performance by department orby employee. This activity can be repeated on a regular basis in amatter of minutes once the initial settings have been stored in thedatabase.

In each of these use cases, the user using the data may performtransactions that are likely to be repeated at a later time. Bycapturing transaction information over time, suggestions associated withthe transactions can be provided to users when a specific context isdetermined, e.g., at a particular stage of use of a particular data set.In some implementations, steps to achieve these results can include thefollowing.

User actions are tracked and recorded. For example, each user action isassumed to be a transaction that is stored along with variousparameters.

Features are ranked according to their importance and usage. Forexample, to suggest transactions to the user in the future, the user'spresent actions can be ranked according to an algorithm that takes intoaccount factors such as the frequency of the user's action, the data itaffects, and how early the action is performed. Based on a transaction'srank, a transaction will be recommended to the user using a userinterface.

Transactions are categorized. For example, to prevent unnecessarysuggestions, all the available actions can be categorized intotransactions which are available to a user at a particular stage ofworking. The transactions in the early stages, for example, can beavailable to the user throughout subsequent stages, while later stagetransactions are not available for earlier stages. In someimplementations, business intelligence analysis can include four stagesin which users first prepare the data, visualize the data, create astory out of the visualizations, and share the stories/visualizations(e.g., with other users).

Different types of transactions can be used in different stagesindividual stages. For example, in the data preparation stage,transactions can include select dataset, select column, select customrange, filter, sort, convert, rename, create measure or dimension,create hierarchy, duplicate, group by range, and other data preparationoperations. Visualize transactions, for example, can correspond tocharts of various types that are made by selecting measures anddimensions. Compose transactions, for example, can allow users topresent their graphs in the form of a board, a report, an infographic,or some other form (e.g., as well as specifying alignment or otherformatting to be used). Share transactions, for example, can correspondto options to share the finished product by exporting the finishedproduct as a file, publishing the finished product to a cloud server,mailing the finished product to one or more recipients, or sharing insome other way. Publishing, for example, can require additional userspecification, e.g., choosing a destination, entering login details,specifying a list of recipients, or providing other information.

The usage data is stored. For example, once transactions are recorded,user actions can be stored in a database (e.g., a light weightdatabase). Subsequent generation of suggestions can make use of thedatabase, e.g., including identifying relevant transactions for the userand running a ranking algorithm to rate suggested transactions for theuser. This database can be updated over time, e.g., adding newtransactions and ranking them in relation to other user actions.

Application overhead can be avoided. For example, processing that isused to record, rank, store and finally recommend transactions can bedone without slowing down the normal functioning of the application.

FIG. 1 is a block diagram of an environment 100 for providingsuggestions. Specifically, the illustrated environment 100 includes, oris communicably coupled with, plural client devices 102, and a server104, connected using a network 108. For example, the environment 100 canbe used to present information on the plural client devices 102 usinginformation available from the server 104. Further, input can bereceived from users 109 on the plural client devices 102 for use by theserver 104. For example, users 109 can use business intelligence toolson the client devices 102, including performing transactions on datasets on which the tools operate. The server 104 can be used, forexample, to store transaction information for transactions occurring onthe client devices 102 and to make suggestions to users 109 based onpreviously-used transactions. In some implementations, the server 104can be replaced, at least in part, using a plugin for a respectivebusiness intelligence tool. Further, part or all of the functionality ofthe server 104 can be included in applications executing on the clientdevices 102.

At a high level, the server 104 comprises an electronic computing deviceoperable to collect, store and provide access to transaction informationfor use by the client device 102. The transaction information can beprovided to the client device 102, for example, in the form ofsuggestions. A transaction repository 111, for example, can includetransaction information received from the plural client devices 102. Forexample, users 109 can provide specific actions on business data 135being used, manipulated or otherwise processed.

As used in the present disclosure, the term “computer” is intended toencompass any suitable processing device. For example, although FIG. 1illustrates a single server 104, the environment 100 can be implementedusing two or more servers 104, as well as computers other than servers,including a server pool. Indeed, the server 104 may be any computer orprocessing device such as, for example, a blade server, general-purposepersonal computer (PC), Macintosh, workstation, UNIX-based workstation,or any other suitable device. In other words, the present disclosurecontemplates computers other than general purpose computers, as well ascomputers without conventional operating systems. Further, illustratedserver 104 may be adapted to execute any operating system, includingLinux, UNIX, Windows, Mac OS®, Java™, Android™, iOS or any othersuitable operating system. According to some implementations, the server104 may also include, or be communicably coupled with, an e-mail server,a web server, a caching server, a streaming data server, and/or othersuitable server(s). In some implementations, components of the server104 may be distributed in different locations and coupled using thenetwork 108.

In some implementations, the server 104 includes an application server112 that performs processing at the server 104 that is needed to receivetransactions from and provide suggestions to the client device 102. Forexample, the application server 112 can receive and store informationcollected over time for transactions that have occurred on the clientdevice 102. Further, the application server 112 can access the storedtransaction information to generate suggestions, as described below.

The application server 112 includes an interface module 116, forexample, that can receive, from the client device 102, transactioninformation. For example, the information received can be informationprovided by the user in a client application 114, such as a front endfor a business intelligence tool. Generally, the interface module 116comprises logic encoded in software and/or hardware in a suitablecombination and operable to communicate with the network 108. Morespecifically, the interface module 116 may comprise software supportingone or more communication protocols associated with communications suchthat the network 108 or the interface's hardware is operable tocommunicate physical signals within and outside of the illustratedenvironment 100.

The application server 112 further includes a transaction module 120.For example, the transaction module 120 can be used for receivingtransaction information for transactions occurring on the client device102 and storing the transactions in a transaction repository 111.

The application server 112 further includes a ranking module 122 thatcan be used to rank transactions and associated suggestions. Theranking, for example, can be used so that suggestions provided to users109 are ranked according to their likely selection for use, such asbased on frequencies of past use and other criteria.

The application server 112 further includes a monitoring module 124. Forexample, the monitoring module 124 can monitor processing on the clientapplication 114 to detect times at which suggestions can be provided,e.g., when the user 109 is at a processing stage in which one or moretransactions used in the past may be used again, depending on a currentcontext.

The application server 112 further includes a suggestion module 129. Asan example, the suggestion module 129 can generate suggestions fortransactions that may be re-used on the client device 102. In someimplementations, the suggestions generated by the suggestion module 129for a particular user can include suggestions based on transactions ofother users who have the same role as the particular user.

The server 104 further includes a processor 126 and memory 128. Althoughillustrated as the single processor 126 in FIG. 1, two or moreprocessors 126 may be used according to particular needs, desires, orparticular implementations of the environment 100. Each processor 126may be a central processing unit (CPU), an application specificintegrated circuit (ASIC), a field-programmable gate array (FPGA), oranother suitable component. Generally, the processor 132 executesinstructions and manipulates data to perform the operations of theclient device 102. Specifically, the processor 126 executes thefunctionality required to receive and process requests from the clientdevice 102 and analyze information received from the client device 102.

The memory 128 (or multiple memories 128) may include any type of memoryor database module and may take the form of volatile and/or non-volatilememory including, without limitation, magnetic media, optical media,random access memory (RAM), read-only memory (ROM), removable media, orany other suitable local or remote memory component. The memory 128 maystore various objects or data, including caches, classes, frameworks,applications, backup data, business objects, jobs, web pages, web pagetemplates, database tables, repositories storing business and/or dynamicinformation, and any other appropriate information including anyparameters, variables, algorithms, instructions, rules, constraints, orreferences thereto associated with the purposes of the server 104. Insome implementations, memory 128 includes the transaction repository111. Other components within the memory 128 are possible.

Each client device 102 of the environment 100 may be any computingdevice operable to connect to, or communicate with, at least the server104 via the network 108 using a wire-line or wireless connection. Ingeneral, the client device 102 comprises an electronic computer deviceoperable to receive, transmit, process, and store any appropriate dataassociated with the environment 100 of FIG. 1.

The illustrated client device 102 further includes a processor 132, amemory 134, and an interface 136. The interface 136 is used by theclient device 102 for communicating with other systems in a distributedenvironment—including within the environment 100—connected to thenetwork 108, e.g., the server 104, as well as other systems communicablycoupled to the network 108 (not illustrated). Generally, the interface136 comprises logic encoded in software and/or hardware in a suitablecombination and operable to communicate with the network 108. Morespecifically, the interface 136 may comprise software supporting one ormore communication protocols associated with communications such thatthe network 108 or interface's hardware is operable to communicatephysical signals within and outside of the illustrated environment 100.

Regardless of the particular implementation, “software” may includecomputer-readable instructions, firmware, wired and/or programmedhardware, or any combination thereof on a tangible medium (transitory ornon-transitory, as appropriate) operable when executed to perform atleast the processes and operations described herein. Indeed, eachsoftware component may be fully or partially written or described in anyappropriate computer language including C, C++, Java™, Visual Basic,assembler, Perl®, any suitable version of 4GL, as well as others. Whileportions of the software illustrated in FIG. 1 are shown as individualmodules that implement the various features and functionality throughvarious objects, methods, or other processes, the software may insteadinclude a number of sub-modules, third-party services, components,libraries, and such, as appropriate. Conversely, the features andfunctionality of various components can be combined into singlecomponents as appropriate.

As illustrated in FIG. 1, the client device 102 includes the processor132. Although illustrated as the single processor 132 in FIG. 1, two ormore processors 132 may be used according to particular needs, desires,or particular implementations of the environment 100. Each processor 132may be a central processing unit (CPU), an application specificintegrated circuit (ASIC), a field-programmable gate array (FPGA), oranother suitable component. Generally, the processor 132 executesinstructions and manipulates data to perform the operations of theclient device 102. Specifically, the processor 132 executes thefunctionality required to send requests to the server 104 and to receiveand process responses from the server 104.

The illustrated client device 102 also includes a memory 134, ormultiple memories 134. The memory 134 may include any memory or databasemodule and may take the form of volatile or non-volatile memoryincluding, without limitation, magnetic media, optical media, randomaccess memory (RAM), read-only memory (ROM), removable media, or anyother suitable local or remote memory component. The memory 134 maystore various objects or data, including caches, classes, frameworks,applications, backup data, business objects, jobs, web pages, web pagetemplates, database tables, repositories storing business and/or dynamicinformation, and any other appropriate information including anyparameters, variables, algorithms, instructions, rules, constraints, orreferences thereto associated with the purposes of the client device102.

The illustrated client device 102 is intended to encompass any computingdevice such as a smart phone, tablet computing device, PDA, desktopcomputer, laptop/notebook computer, wireless data port, one or moreprocessors within these devices, or any other suitable processingdevice. For example, the client device 102 may comprise a computer thatincludes an input device, such as a keypad, touch screen, or otherdevice that can accept user information, and an output device thatconveys information associated with the operation of the server 104 orthe client device 102 itself, including digital data, visualinformation, or a graphical user interface (GUI) 140, as shown withrespect to and included by the client device 102. The GUI 140 interfaceswith at least a portion of the environment 100 for any suitable purpose,including generating user interface screens that support userinteraction with the client application 114 (e.g., a businessintelligence tool). A presentation module 118, for example, can be acomponent of the client application 114 that controls presentation ofinformation to the use in the GUI 140. The presentation module 118 canalso handle inputs received from the user and provide suggestions fordisplay to the user.

FIG. 2 is a flow diagram for an example process 200 for recordingtransaction information for a user action. The user action, for example,can be a user action such as filtering a spreadsheet according to afilter. The user action can be performed, for example, by User A, suchas while User A is working with marketing data and using a BI tool.

At 202, the process 200 can start. For example, the transactionrepository 111 can be at an initial state in which no transactions havebeen recorded, e.g., for User A or other users.

At 204, transaction attributes are initialized. The transaction module120, for example, can receive information for a transaction that hasjust occurred on the client device 102. Transaction information for thetransaction can include, for example, a user identifier for the user whoperformed the transaction, stage information that identifies at leastthe stage in which the transaction occurred, an operation (e.g.,filtering a data set) associated with the transaction, and associatedparameters (e.g., the filtering criteria).

At 206, a determination is made whether any transaction informationexists for the particular user for which the current transaction isbeing recorded. In some implementations, determining if transactionsexist can include determining that there are zero rows in thetransaction repository 111 for the user (e.g., User A). The transactionrepository 111 can be at an initial state, for example, whentransactions have yet to be recorded for User A.

At 208, e.g., if it has been determined that no transaction informationexists for the particular user, a rank is calculated. The rank in thiscase can be a default rank, as the transaction information to be storedin the transaction repository 111 as the first and only transaction,e.g., for User A.

At 210, the transaction information is stored. For example, thetransaction module 120 can store the transaction information in thetransaction repository 111, e.g., as a new inserted row as the firststored transaction for User A. The stored transaction information, forexample, can indicate that User A performed a particular user action,e.g., a filtering operation on marketing data.

At 212, the process 200 can end. For example, the process 200 can endregardless of whether the first transaction is being recorded, or thetransaction being recorded is identical to a previously-storedtransaction, at which time new ranks can be calculated, as described forsteps 214-222.

At 214, e.g., if it has been determined that transaction informationdoes already exist for the particular user (e.g., at least one rowexists for the user in the transaction repository 111), a determinationis made whether the particular transaction is in the transactionrepository. The transaction module 120 can make the determination, forexample, by looking for existing entries in the transaction repository111 for entries having the user identifier for User A and matching stageinformation, a matching action, and matching parameters.

At 216, e.g., if it has been determined that the particular transactionalready exists for the particular user, the rank associated with thetransaction is edited. For example, the ranking module 122 can increasethe rank for the transaction because the transaction has been used anadditional time by User A.

At 218, the existing transaction is updated. For example, thetransaction module 120 can update the corresponding information in thetransaction repository 111, including the updated rank. As theprocessing is now complete, the process can end at step 212.

At 220, e.g., if it has been determined that the particular transactiondoes not yet exist for the particular user, a new rank is calculated.For example, the ranking module 122 can calculate a rank for thetransaction. The calculated rank can depend, for example, on the ranksof existing transactions that are stored for User A.

At 222, a new entry for the transaction is added to the repository. Forexample, the transaction module 120 can store the new transaction in thetransaction repository 111. As the processing is now complete, theprocess can end at step 212.

FIG. 3 is a flow diagram for an example process 300 for makingsuggestions and processing a selected suggestion. For example, theprocess 300 can be a process implemented as a method for providingsuggestions, e.g., consistent with the last method in the code snippetprovided below that makes a determination whether or not to providesuggestions. In some implementations, suggestions can be provided when,for example, an event proposed by a user is not compatible with adocument that the user is processing. As an example, the event can be auser action (e.g., a user-input formula on multiple columns) that is notcompatible with the user's document (e.g., not compatible with data inthe spreadsheet). In some implementations, suggestions can be requestedby the user at any time, such as to obtain relevant suggestions whilethe user is operating on specific data (e.g., a particular column in aspreadsheet) and while the user's progress is at a particular stage(e.g., preparation of the data).

At 302, the process 300 can start. For example, the monitoring module124 can start the process 300 when it is determined that processing bythe user is at a stage at which suggestions may be made, e.g., the useris getting ready to perform a user action.

At 304, information associated for the user's current session isinitialized. For example, the server 104 can provide information to themonitoring module 124 that indicates the document on which the user isworking (e.g., a marketing data spreadsheet) and an event that has or isabout to occur (e.g., a user-input formula on multiple columns).

At 306, the transaction repository is loaded. For example, in support ofthe user's current session on the client device 102, the suggestionmodule 129 can load information from the transaction repository 111 thatis relevant to the user and may be used for providing a suggestion.

At 308, a determination is made whether compatibility exists regardingthe user's proposed user action. The compatibility determination ismade, for example, between a command that the user is about to executeand the document and underlying data that is to be operated on by thecommand.

At 310, e.g., if at 308 compatibility was determined not to existbetween the document and the user's proposed command, the newtransaction can be registered. For example, the transaction module 120can add the new command to the transaction repository 111.

At 312, the process can end. For example, if the new transaction hasbeen registered at 310, then the process 300 can end without havingprovided any suggestions.

At 314, a category is defined. For example, the suggestion module 129can identify a category based on the user identifier associated withUser A and a current stage that User A is in. The identified categorycan also depend on other factors, such as the type of data that User Ais working with. The identified category in this example can be alongthe lines of User=User A, Stage=Visualization, Data=Marketing. In someimplementations, the type of data that is being used can be identified,for example, by the names of one or more tables in a database or inother ways.

At 316, transactions in the repository are filtered by category. Forexample, the suggestion module 129 can identify transactions in thetransaction repository 111 that match User=User A, Stage=Visualization,Data=Marketing. Filtering by stage can include, for example, includingtransactions for stages that are compatible with the current stage.

At 318, the identified transactions are ranked. The ranking module 122,for example, can rank the transactions to create ranked suggestions. Forexample, the highest-ranked suggestion may correspond to the action theUser A has performed more than the other user actions in the samecategory.

At 320, the ranked suggestions are published. As an example, theinterface module 116 can provide the ranked transactions to clientapplication 114 for presentation to the user.

At 322, a user selection of a particular suggestion is received. Forexample, the interface module 116 can receive, from the clientapplication 114, a selection by the user of a particular one of theranked transactions.

At 324, an acceptance of the suggestion is received. For example, theinterface module 116 can receive an election by User A to execute theuser action associated with the selected suggestion. If User A does notaccept the suggestion, for example, then the process 300 can end at 312.

At 326, a favorite indicator for the suggestion is updated. For example,the transaction module 120 can increment a counter associated with thenumber of times that the user action has been performed (e.g., from asuggestion). In some implementations, updating the favorite indicatorcan depend on a user's indication that a user action is a favoriteaction and/or should otherwise be presented as a suggestion in thefuture.

At 328, the transaction is registered. The transaction module 120, forexample, can update the transaction repository 111 with information thatthe transaction has been executed. Once the transaction repository 111has been updated, the process 300 can end at 312.

FIG. 4 shows an example user interface 400 for working with businessdata from which transaction information is obtained. For example, theuser interface 400 includes a campaign analysis screen 402 in which auser can display and work with data in a data area 404. The data caninclude, for example, marketing data that measures the performance of acampaign. The user may interact with the data, for example, in stages,such as stages indicated by stage controls 406 (e.g., prepare,visualize, compose, and share). The data presented in the data area 404can be presented, at least in a current preparation stage, as rows andcolumns in a spreadsheet, data table, or other structure. For example,marketing data shown in the data area 404 includes data rows organizedin columns, e.g., labeled as source, date, city, region and sub-regionfor columns 410-418, respectively. Other columns are possible, e.g., todifferentiate among or provide more information for the entries in thedata area 404 that are indicated by rows.

Transactions can be captured, for example, when the user is using thecampaign analysis screen 402. The transaction module 120, for example,can receive and store transaction information for, e.g., for toptransactions 420, for User A when the user is displaying marketing dataand for a particular stage (e.g., when the stage is the prepare stage406 a).

As shown in FIG. 4, the top transactions 420 include a suggestionidentifier 422 (e.g., for sequentially numbering suggestions), a featurename 424, a favorites count 426, a frequency count 428, a referencescount 430, parameters 432, a column name 434, a category 436, and a rank438. The numbering of the transactions indicated by the suggestionidentifier 422 can depend on the rank 438 that can be re-calculated bythe ranking module 122 each time transaction information for atransaction is stored or updated. For example, the highest rankedsuggestion can have a rank 438 and a suggestion identifier 422, eachhaving the value of one.

Operations performed by the user, for example, can include operationsfor particular ones of measures 440. Example measures can be related toperformance results of a campaign, e.g., including a number ofinteractions, a number of leads, a number of opportunities, a customersatisfaction score, negative mentions, positive mentions, and asentiment. Dimensions 442, for example, can include different ways forthe user to group and/or annotate data. For example, dimensions caninclude regions and other geographic indicators, date/time information(e.g., year, quarter, month, week, day(s) or other periods) andcategories of actions (e.g., interactions, leads, opportunities).

FIGS. 5A-5B collectively show an example user interface 500 forproviding suggestions and determining a suggestion selected by the user.For example, the user interface 500 includes the campaign analysisscreen 402 in which a user can display and work with data in the dataarea 404. The current stage, as shown in FIG. 5A, is the prepare stage406 a. Columns displayed in the data area 404 of the user interface 500include an opportunity 502, a forecast 504, a phase 506, a month column508, a customer name 510, a region 512 and a product 514. Other columnsare possible for use by the user in processing the data, such as columnsdevoted to a quarter, a year, a state and a country.

Suggestions can be available, for example, when the user is displayingthe user interface 500. The suggestion module 129, for example, cangenerate suggestions (e.g., for top transactions 420) for User A whenthe user is displaying marketing data during a particular stage (e.g.,when the stage is the prepare stage 406 a). A different set ofsuggestions would be provided, for example, when the user is in adifferent one of the stages (e.g., visualize, compose, or share).

Examples of suggestions that may be provided include suggestionsassociated with the top transactions 420. In some implementations,suggestion can be provided to the user in an interface, such as asuggestions popup 516 (See FIG. 5B) or other control, that listsspecific information about each suggestion. Suggestions listed in thesuggestions popup 516, for example, correspond to the transactionsdescribed above with reference to FIG. 4. For example, suggested featurenames 518 can correspond to corresponding ones of the feature names 424.Further, the suggestions that appear in the suggestions popup 516 can beranked by the rank 438.

As shown in FIG. 5A, a month dimension 442 a is selected, whichcorresponds to the month column 508. In some implementations, the usercan limit suggestions that are to be presented by selecting a particularcolumn or other entity on the screen.

The suggestions popup 516, for example, can include suggestions 516a-516 d, each associated with a different operation, such as applying agroup-by range, applying a measure (e.g., one of the measures 440),applying a numeric filter, or applying a duplication, respectively.

FIG. 6 is a diagram of an example hierarchical structure 600 formaintaining transaction information. For example, the hierarchicalstructure 600 is shown as a tree consisting of nodes. A similar tree (orother structure for organizing transaction data) can exist for eachuser. Over time, e.g., as a user performs user actions, leaf nodes andother nodes can be continuously added to the tree, each representing adifferent type of user action. Further, nodes in the tree can beupdated, e.g., to increment a number of times that a transaction hasoccurred and/or a suggestion has been selected.

A root node 602, for example, can represent a particular user. Objectson which the user has operated can be represented as object nodes 604,such as including a node 604 a associated with marketing data. Otherones of the object nodes 604 can correspond to other types of data.Object nodes 604 (or other intermediate nodes in the tree) can includeor represent specific operations performed on objects, such as nodescorresponding to selecting a dataset, selecting a column in the dataset, selecting a custom range in the data set, filtering the data set,sorting the data set, converting the data set, renaming the data set,create a measure or dimension for the data set, creating a hierarchy forthe data set, duplicating data in the data set, and grouping data byrange in the dataset. Parameter nodes 606, for example, can besubordinate to the object nodes 604 (e.g., also including operations).For example, for a given object and operation, the parameter nodes 606can identify information such as what measure the user used, whatdimension the user generally has in his visualization, what kind offilter the user applies on a dataset, and what formula the user applieson a column. Other types of nodes are possible, and otherrepresentations are possible for representing transactions andtransaction information for a given user. In some implementations, nodesof trees of different users can be linked, e.g., if user actions andsuggestions are sharable across groups of users, users in particularroles, or other types of shared information.

FIG. 7 is a flowchart of an example method 700 for providingsuggestions. For clarity of presentation, the description that followsgenerally describes method 700 in the context of FIGS. 1-6. However, itwill be understood that the method 700 may be performed, for example, byany other suitable system, environment, software, and hardware, or acombination of systems, environments, software, and hardware asappropriate. For example, the server 104 and/or its components can beused to execute the method 700.

At 702, transaction information is received that is associated with useractions during use by a user of a business intelligence tool. Each useraction is associated with an operation in a particular stage ofprocessing on business data obtained from one or more databases. Thetransaction information for a particular user action includes, forexample, a user identifier identifying the user performing theparticular user action, stage information identifying the particularstage of the processing in which the particular user action occurs, theoperation associated with the particular user action, and one or moreparameters used in the operation. For example, interface module 116 canreceive, e.g., from the client application 114, information thatidentifies a user action that the user performed, such as a filteringcommand executed in a spreadsheet. The received information can identifythe user, for example, as User A, and further identify a context of theuser action. The context, for example, can include stage information(e.g., identifying the current stage as a data preparation stage), aparticular operation (e.g., filtering), and parameters (e.g., afilter-by argument).

In some implementations, stages are sequential and include a datapreparation stage in which data is prepared, a visualization stage inwhich data is presented in a visualization to the user, a synthesisstage in which a business story is created from one or morevisualizations, and a sharing stage in which business informationassociated with previous stages is shared. For example, stages can belabeled within a user interface (e.g., for a spreadsheet) associatedwith the business intelligence tool.

In some implementations, user actions in the data preparation stageinclude selecting a dataset, selecting a column in the data set,selecting a custom range in the data set, filtering the data set,sorting the data set, converting the data set, renaming the data set,create a measure or dimension for the data set, creating a hierarchy forthe data set, duplicating data in the data set, and grouping data byrange in the dataset. For example, the user action identified for thetransaction can be the action of “filtering the data set” such as if theuser is filtering data in a spreadsheet by a particular argument orvalue.

At 704, the transaction information is stored. The transaction module120, for example, can store the transaction information in thetransaction repository 111. In some implementations, before storage ofthe transaction information, a rank (or a new rank) can be calculatedfor the transaction. The rank and other information stored for eachtransaction can be used to determine how information from thetransaction repository 111 is used later to generate suggestions.

At 706, subsequent user actions by the user are monitored, includingdetermining a time at which stage conditions associated with a currentsession for the user match the user and the stage information in thestored transaction information. For example, the monitoring module 124can monitor user actions and current stage information, e.g., during asession by the user using the client application 114.

At 708, in response to determining that matching stage conditions exist,additional processing occurs. For example, the monitoring module 124 candetermine that conditions exist for which suggestions are to bedetermined for the user, such as when the user is at a certain point inusing data within the client application 114.

At 710, pertinent transactions associated with the matching stageconditions are identified. The transaction module 120, for example, canidentify matching transaction information from the transactionrepository 111 that correspond to the user (e.g., user identifier=UserA) and that match a current stage (e.g., the user is in the DataPreparation stage).

At 712, one or more suggestions are created for presentation to theuser. Each suggestion of the one or more suggestions is associated withgroups of one or more transactions of the pertinent transactions. Forexample, the suggestion module 129 can prepare a set of suggestions thatare to be provided to the user.

At 714, the one or more suggestions are provided for presentation to theuser. For example, the application server 112 can provide thesuggestions to the client device 102, e.g., for presentation to the userusing the client application 114. The suggestions can be presented tothe user, for example, in the suggestions popup 516.

In some implementations, the method 700 further includes, afterproviding the one or more suggestions, additional steps. Anidentification of a particular suggestion is received from the providedone or more suggestions. Using the identified particular suggestion, anew operation is performed that uses transaction information for theparticular suggestion in a new operation on business data beingmanipulated by the user. Performing the operation includes using the oneor more parameters from the stored transaction information. The storedtransaction information for the particular suggestion is updated toindicate an additional use of an associated user action. For example,the user can select one of the suggestions in the suggestions popup 516.Upon selection of the particular suggestion, information associated withthe suggestion can be used to perform the corresponding operation (e.g.,filtering a data set by a filter parameter). Because the suggestion hasbeen used, information for the associated transaction can be updated inthe transaction repository, e.g., incrementing a count regarding thenumber of times that the transaction has occurred.

In some implementations, the method 700 further includes ranking thesuggestions to produce ranked suggestions. For example, each rankedsuggestion can be ranked according to a transaction rank determined fromone or more of a favorite index, a frequency count, and a referencescount. For example, the ranking module 122 can rank the set ofsuggestions that are to be provided to the user. The rank can be based,for example, on a formula (e.g., weighted formula) that is a function ofthe number of times that the user has indicated that the transaction isa favorite, the frequency count, and the references count. Thereferences count, for example, indicates the number of times thetransaction is indirectly referred to in other transactions. Forexample, a transaction of a filter and a transaction of applying aformula on a filtered column may be identified. Here, filtering thecolumn is indirectly referred to by applying the formula, and wouldrepresent a reference count. Conversely, the frequency count identifiesthe number of times the transaction occurs.

In some implementations, the one or more suggestions provided to theuser include, for each ranked suggestion, a suggestion identifier, afeature name, the favorite index, the frequency count, the referencescount, one or more parameters, one or more column names associated withthe one or more parameters, a stage category, and a transaction rank.For example, the suggestions popup 516 can include (or provide accessto) specific information associated with each suggestion.

In some implementations, each ranked suggestion is further ranked usinga measure of time since an occurrence of an associated user action. Forexample, a transaction that has occurred four times very recently may beranked higher as a suggestion than a transaction that occurred fivetimes, e.g., if those five times were several days ago.

The steps of the example method 700 provide a few implementations thatcan be used for generating suggestions. Other implementations arepossible, and may be implemented using code that is provided in a BItool as a plug-in, for example.

In some implementations, the following code snippet or some other codecan be used for handling (processing, storing, accessing) transactionsand generating suggestions based on the transactions:

class Transaction {   int id,String feature, String columnName , String[ ] parameters , int trsanaction_parameter_frequency,int favourit, intfeature_frequency,String category,int rank;   Transaction(int id,Stringfeature, String columnName , String [ ] parameters , inttrsanaction_parameter_frequency,int favourit, intfeature_frequency,String category,int rank) {   this.id = id;   }  public int getId( ) {    return this.id;   }   public StringgetColumn( ) {    return this.columnName;   }   public String [ ]getParameters( ) {    return this.parameters;   }   public StringgetFeature( ) {    return this.feature;   }   public String getCategory() {    return this.category;   }    public int getFavourit( ) {       return this.favourit;    }    public void setFavourit(intfavourit) {    this.favourit = favourit;    } } class Transactions {  public ArrayList<String> transactionColumnList = new ArrayList<String> ( );   public int rowCount;   private static Transactionstransactions;   public static Transactions getInstance( ) {    if( null== transactions) {      transactions = new Transactions( );    }   return transactions;   }   Transactions ( ) {    // load transactionfrom database        // -- connect to transaction database at launchtime        // -- read result set        //-- construct Transactionobject for each row        // -- register Transaction into Transactionsobject   this.rowCount = databaseRowCount;   }   HashMap <int,Transaction> transactionMap = new HashMap <int, TransactionMap>;  public void registerTransaction (Transaction newTrsansaction) {   transactionMap.put(newTrsansaction.getId( ),newTrsansaction);   this.putColumnName(newTrsansaction.getColumn( ));   }   publicTransaction getTransaction(int id) {    return transactionMap.get(id);  }   public void serailizeTransactions( ) {    // write eachtrsnsaction to database row by row   }   public voidputColumnName(String columnName) {   transactionColumnList.put(columnName)   }   public ArrayList<String>getColumnList( ) {    return transactionColumnList;   } }/**************************************************/ publicRegisterTransaction{  //constants final int BASE_WEIGHT = 500;   finalint FAVOURTIE_WEIGHT = 100;   final inttrsanaction_parameter_frequency_WEIGHT = 20;   final intPARAMETER_WEIGHT = 10;   final int feature_frequency_WEIGHT = 5;   finalint ORDER_WEIGHT = 1; static int id = 1; registerTransaction(Event event, Transactions transactions) {   boolean includeNewTransaction = true;   Transactions transactions = transactions;    int id = 1,Stringfeature = event.getFeature, String columnName = event.getColumn , String[ ] parameter = event.getParameters,    inttrsanaction_parameter_frequency = 1,int favourit = 0, intfeature_frequency = 0,long rank;    String category = event.getRoomName;   if(transactions.rowCount == 0) {      // register new transaction     rank = getRank(id ,trsanaction_parameter_frequency ,favourite,feature_frequency, parameter_count);     transactions.registerTransaction(new Transaction(id , feature,columnName , parameter , trsanaction_parameter_frequency , favourit ,feature_frequency , category , rank));      booleanincludeNewTransaction = false;    }    else {    // Check if the currtransaction already exists in the db. if yes then update the existingtransaction else insert      for (int transaction_counter = 0 ;transaction_counter < transactions.rowCount ; transaction_counter ++ )       {         Transaction transaction =transactions.getTransaction(transaction_counter);         parameter =event.getParameters;         // update feature frequency if transactionalready exist.         if(transaction.getFeature( ).equals(feature)) {            feature_frequency = transaction.getfeature_frequency( ) + 1;        // update transaction_param frequency if both transaction andparameter already exist.           if(transaction.ColumnName().equals(columnName)) {             trsanaction_parameter_frequency =transaction.gettrsanaction_parameter_frequency( ) + 1;           }          int rank = rank = getRank(id ,trsanaction_parameter_frequency,favourite ,feature_frequency, parameter_count);           // upateexisiting transaction if frequencies are updated           TransactionupdateExisitingTransaction = transactions.registerTransaction(newTransaction(transaction_counter , feature, columnName , parameter ,trsanaction_parameter_frequency , favourit , feature_frequency ,category ,rank))           boolean includeNewTransaction = false;        }      }      if(  includeNewTransaction) {        //calc andstore rank and increment id        rank = getRank(transactions.rowCount,trsanaction_parameter_frequency, favourite ,feature_frequency,parameter_count);        transactions.registerTransaction(newTransaction(transactions.rowCount , feature, columnName , parameter ,trsanaction_parameter_frequency , favourit , feature_frequency ,category ,rank))      }    }    id++; }   public int getRank(int id ,long base_weight , int trsanaction_parameter_frequency , inttrsanaction_parameter_frequency_weight , int favourit , intfavourit_weight, int feature_frequency , int feature_frequency_weight,int parameter_count , int parameter ){    int rank = BASE_WEIGHT − (id *ORDER_WEIGHT) + trsanaction_parameter_frequency *trsanaction_parameter_frequency_WEIGHT + parameter_count *PARAMETER_WEIGHT                          + (favourite? 1:0) *FAVOURTIE_WEIGHT + feature_frequency * feature_frequency_WEIGHT;   } }/**************************************************************/ classsuggestTransaction {   suggesttionTransaction(document,event) {   Transactions transactions = Transactions.getInstance( );    booleanisDocumentCompatible = isDocumentCompatible(transactions,event);    //check if newly acquired document is subset or superset of transactionobject    if (!isDocumentCompatible) {        newregisterTransaction(transactions,event , 0);      }    else {       String current_category = event.getRoomName;        // first loadonly top 5 suggestions for given category.        Iterator <Transaction>transactionItr = transactions.getColumnList( ).iterator( );       ArrayList<Transaction>suggestionListForRoom = newArrayList<Transaction> ( );        while ( transactionItr.hasNext( )) {        Transaction currentTransaction = transactionItr.next( );        if(currentTransaction.getCategory( ).equals(current_category)) {          suggestionListForRoom.add(currentTransaction);         }       }        publishSuggestionEvent(transactions,suggestionListForRoom , callback); // the suggestions are sent to userand callback is awaited.    // we can also suppress the suggestionsafter given time    }   }   public booleanisDocumentCompatible(transactions,event) {    ArrayList<String>current_Document_ColumnName = event.getColumnList;    ArrayList<String>transaction_db_ParamName = transactions.getColumnList( );    return(current_Document_ColumnName.containsAll(transaction_db_ParamName)     || transaction_db_ParamName.containsAll(transaction_db_ParamName));   }   public void publishSuggestionEvent(ArrayList<Transaction>suggestionListForRoom, Event callback)    if(!callback.equals(“−1”) {       Transaction selectedTransaction =suggestionListForRoom.get(callback.id);        int favourit =selectedTransaction.getFavourit( ) + 1;       selectedTransaction.setFavourit(favourit);        // update thetransaction database;        //apply the transaction       applyTransaction(selectedTransaction);        // in prepare room-> display both transaction and param        // in visualization room ->display either only charts or chart + parama        // in share room ->suggest emails, top server details       transactions.registerTransaction(selectedTransaction);    } }

In some implementations, components of the environments and systemsdescribed above may be any computer or processing device such as, forexample, a blade server, general-purpose personal computer (PC),Macintosh, workstation, UNIX-based workstation, or any other suitabledevice. In other words, the present disclosure contemplates computersother than general purpose computers, as well as computers withoutconventional operating systems. Further, components may be adapted toexecute any operating system, including Linux, UNIX, Windows, Mac OS®,Java™, Android™, iOS or any other suitable operating system. Accordingto some implementations, components may also include, or be communicablycoupled with, an e-mail server, a Web server, a caching server, astreaming data server, and/or other suitable server(s).

Processors used in the environments and systems described above may be acentral processing unit (CPU), an application specific integratedcircuit (ASIC), a field-programmable gate array (FPGA), or anothersuitable component. Generally, each processor can execute instructionsand manipulates data to perform the operations of various components.Specifically, each processor can execute the functionality required tosend requests and/or data to components of the environment and toreceive data from the components of the environment, such as incommunications between the external, intermediary and target devices.

Components, environments and systems described above may include amemory or multiple memories. Memory may include any type of memory ordatabase module and may take the form of volatile and/or non-volatilememory including, without limitation, magnetic media, optical media,random access memory (RAM), read-only memory (ROM), removable media, orany other suitable local or remote memory component. The memory maystore various objects or data, including caches, classes, frameworks,applications, backup data, business objects, jobs, web pages, web pagetemplates, database tables, repositories storing business and/or dynamicinformation, and any other appropriate information including anyparameters, variables, algorithms, instructions, rules, constraints, forreferences thereto associated with the purposes of the target,intermediary and external devices. Other components within the memoryare possible.

Regardless of the particular implementation, “software” may includecomputer-readable instructions, firmware, wired and/or programmedhardware, or any combination thereof on a tangible medium (transitory ornon-transitory, as appropriate) operable when executed to perform atleast the processes and operations described herein. Indeed, eachsoftware component may be fully or partially written or described in anyappropriate computer language including C, C++, Java™, Visual Basic,assembler, Perl®, any suitable version of 4GL, as well as others.Software may instead include a number of sub-modules, third-partyservices, components, libraries, and such, as appropriate. Conversely,the features and functionality of various components can be combinedinto single components as appropriate.

Devices can encompass any computing device such as a smart phone, tabletcomputing device, PDA, desktop computer, laptop/notebook computer,wireless data port, one or more processors within these devices, or anyother suitable processing device. For example, a device may comprise acomputer that includes an input device, such as a keypad, touch screen,or other device that can accept user information, and an output devicethat conveys information associated with components of the environmentsand systems described above, including digital data, visual information,or a graphical user interface (GUI). The GUI interfaces with at least aportion of the environments and systems described above for any suitablepurpose, including generating a visual representation of a Web browser.

The preceding figures and accompanying description illustrate exampleprocesses and computer implementable techniques. The environments andsystems described above (or their software or other components) maycontemplate using, implementing, or executing any suitable technique forperforming these and other tasks. It will be understood that theseprocesses are for illustration purposes only and that the described orsimilar techniques may be performed at any appropriate time, includingconcurrently, individually, in parallel, and/or in combination. Inaddition, many of the operations in these processes may take placesimultaneously, concurrently, in parallel, and/or in different ordersthan as shown. Moreover, processes may have additional operations, feweroperations, and/or different operations, so long as the methods remainappropriate.

In other words, although this disclosure has been described in terms ofcertain implementations and generally associated methods, alterationsand permutations of these implementations, and methods will be apparentto those skilled in the art. Accordingly, the above description ofexample implementations does not define or constrain this disclosure.Other changes, substitutions, and alterations are also possible withoutdeparting from the spirit and scope of this disclosure.

What is claimed is:
 1. A method comprising: receiving transactioninformation associated with user actions during use by a first user of abusiness intelligence tool, each user action associated with anoperation in a particular stage of processing on business data obtainedfrom one or more databases, the transaction information for a particularuser action including: a user identifier identifying the first userperforming the particular user action; stage information identifying theparticular stage of the processing in which the particular user actionoccurs; the operation associated with the particular user action; andone or more parameters used in the operation; storing the transactioninformation; monitoring subsequent user actions by the first user in acurrent session, wherein monitoring subsequent user actions includesdetermining a time at which stage conditions associated with the currentsession for the first user match a portion of the stored transactioninformation corresponding to the first user and the stage information;and in response to determining that stage conditions exist in the storedtransaction information that match the first user and the stageconditions of the current context: identifying pertinent transactionsassociated with the matching stage conditions; creating one or moresuggestions for presentation to the first user, each suggestion of theone or more suggestions being associated with groups of one or moretransactions of the pertinent transactions; and providing the one ormore suggestions for presentation to the first user.
 2. The method ofclaim 1, further comprising, after providing the one or moresuggestions: receiving an identification of a particular suggestion fromthe provided one or more suggestions; performing, using the identifiedparticular suggestion, a new operation using transaction information forthe particular suggestion in a new operation on business data beingmanipulated by the first user, including using the one or moreparameters from the stored transaction information; and updating thestored transaction information for the particular suggestion to indicatean additional use of an associated user action.
 3. The method of claim1, wherein stages are sequential and are selected from the groupcomprising a data preparation stage in which data is prepared, avisualization stage in which data is presented in a visualization to thefirst user, a synthesis stage in which a business story is created fromone or more visualizations, and a sharing stage in which businessinformation associated with previous stages is shared.
 4. The method ofclaim 3, wherein user actions in the data preparation stage includeactions selected from a group comprising: selecting a dataset, selectinga column in the data set, selecting a custom range in the data set,filtering the data set, sorting the data set, converting the data set,renaming the data set, create a measure or dimension for the data set,creating a hierarchy for the data set, duplicating data in the data set,and grouping data by range in the dataset.
 5. The method of claim 1,further comprising ranking the suggestions to produce rankedsuggestions.
 6. The method of claim 5, wherein each ranked suggestion isranked according to a transaction rank determined from one or more of afavorite index, a frequency count, and a references count.
 7. The methodof claim 6, wherein the one or more suggestions provided to the firstuser include, for each ranked suggestion, a suggestion identifier, afeature name, the favorite index, the frequency count, the referencescount, one or more parameters, one or more column names associated withthe one or more parameters, a stage category, and a transaction rank. 8.The method of claim 6, wherein each ranked suggestion is further rankedusing a measure of time since an occurrence of an associated useraction.
 9. A system comprising: at least one memory storing: atransaction repository defining transaction information received fromthe plural client devices, including information for specific useractions on business data being used, manipulated or otherwise processed;and an application for: receiving the transaction information associatedwith user actions during use by a first user of a business intelligencetool, each user action associated with an operation in a particularstage of processing on business data obtained from one or moredatabases, the transaction information for a particular user actionincluding: a user identifier identifying the first user performing theparticular user action; stage information identifying the particularstage of the processing in which the particular user action occurs; theoperation associated with the particular user action; and one or moreparameters used in the operation; storing the transaction information;monitoring subsequent user actions by the first user in a currentsession, wherein monitoring subsequent user actions includes determininga time at which stage conditions associated with the current session forthe first user match a portion of the stored transaction informationcorresponding to the first user and the stage information; and inresponse to determining that stage conditions exist in the storedtransaction information that match the first user and the stageconditions of the current context: identifying pertinent transactionsassociated with the matching stage conditions; creating one or moresuggestions for presentation to the first user, each suggestion of theone or more suggestions being associated with groups of one or moretransactions of the pertinent transactions; and providing the one ormore suggestions for presentation to the first user.
 10. The system ofclaim 9, further comprising in the application, after providing the oneor more suggestions: receiving an identification of a particularsuggestion from the provided one or more suggestions; performing, usingthe identified particular suggestion, a new operation using transactioninformation for the particular suggestion in a new operation on businessdata being manipulated by the first user, including using the one ormore parameters from the stored transaction information; and updatingthe stored transaction information for the particular suggestion toindicate an additional use of an associated user action.
 11. The systemof claim 9, wherein stages are sequential and are selected from thegroup comprising a data preparation stage in which data is prepared, avisualization stage in which data is presented in a visualization to thefirst user, a synthesis stage in which a business story is created fromone or more visualizations, and a sharing stage in which businessinformation associated with previous stages is shared.
 12. The system ofclaim 11, wherein user actions in the data preparation stage includeactions selected from a group comprising: selecting a dataset, selectinga column in the data set, selecting a custom range in the data set,filtering the data set, sorting the data set, converting the data set,renaming the data set, create a measure or dimension for the data set,creating a hierarchy for the data set, duplicating data in the data set,and grouping data by range in the dataset.
 13. The system of claim 9,further comprising in the application ranking the suggestions to produceranked suggestions.
 14. The system of claim 13, wherein each rankedsuggestion is ranked according to a transaction rank determined from oneor more of a favorite index, a frequency count, and a references count.15. A non-transitory computer-readable media encoded with a computerprogram, the program comprising instructions that when executed by oneor more computers cause the one or more computers to perform operationscomprising: receiving transaction information associated with useractions during use by a first user of a business intelligence tool, eachuser action associated with an operation in a particular stage ofprocessing on business data obtained from one or more databases, thetransaction information for a particular user action including: a useridentifier identifying the first user performing the particular useraction; stage information identifying the particular stage of theprocessing in which the particular user action occurs; the operationassociated with the particular user action; and one or more parametersused in the operation; storing the transaction information; monitoringsubsequent user actions by the first user in a current session, whereinmonitoring subsequent user actions includes determining a time at whichstage conditions associated with the current session for the first usermatch a portion of the stored transaction information corresponding tothe first user and the stage information; and in response to determiningthat stage conditions exist in the stored transaction information thatmatch the first user and the stage conditions of the current context:identifying pertinent transactions associated with the matching stageconditions; creating one or more suggestions for presentation to thefirst user, each suggestion of the one or more suggestions beingassociated with groups of one or more transactions of the pertinenttransactions; and providing the one or more suggestions for presentationto the first user.
 16. The non-transitory computer-readable media ofclaim 15, the operations further comprising, after providing the one ormore suggestions: receiving an identification of a particular suggestionfrom the provided one or more suggestions; performing, using theidentified particular suggestion, a new operation using transactioninformation for the particular suggestion in a new operation on businessdata being manipulated by the first user, including using the one ormore parameters from the stored transaction information; and updatingthe stored transaction information for the particular suggestion toindicate an additional use of an associated user action.
 17. Thenon-transitory computer-readable media of claim 15, wherein stages aresequential and are selected from the group comprising a data preparationstage in which data is prepared, a visualization stage in which data ispresented in a visualization to the first user, a synthesis stage inwhich a business story is created from one or more visualizations, and asharing stage in which business information associated with previousstages is shared.
 18. The non-transitory computer-readable media ofclaim 17, wherein user actions in the data preparation stage includeactions selected from a group comprising: selecting a dataset, selectinga column in the data set, selecting a custom range in the data set,filtering the data set, sorting the data set, converting the data set,renaming the data set, create a measure or dimension for the data set,creating a hierarchy for the data set, duplicating data in the data set,and grouping data by range in the dataset.
 19. The non-transitorycomputer-readable media of claim 15, the operations further comprisingranking the suggestions to produce ranked suggestions.
 20. Thenon-transitory computer-readable media of claim 19, wherein each rankedsuggestion is ranked according to a transaction rank determined from oneor more of a favorite index, a frequency count, and a references count.